A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration

TL;DR

This paper introduces DstruCCN, a deep neural network combining CNN and Transformer models to improve single-sequence protein secondary structure prediction and 3D structure reconstruction.

Contribution

It presents a novel fusion model integrating CNN and Transformer for enhanced protein structure prediction from single sequences.

Findings

Improved accuracy in secondary structure prediction.

Effective reconstruction of 3D protein structures.

Demonstrated superiority over existing methods.

Abstract

Proteins are essential for life, and their structure determines their function. The protein secondary structure is formed by the folding of the protein primary structure, and the protein tertiary structure is formed by the bending and folding of the secondary structure. Therefore, the study of protein secondary structure is very helpful to the overall understanding of protein structure. Although the accuracy of protein secondary structure prediction has continuously improved with the development of machine learning and deep learning, progress in the field of protein structure prediction, unfortunately, remains insufficient to meet the large demand for protein information. Therefore, based on the advantages of deep learning-based methods in feature extraction and learning ability, this paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction. The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization.